






'''simple docstring'''







import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : str      			=     None
  _a  : List[str]      			=     None







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self.feat_extract_tester.prepare_feat_extract_dict()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'feature_size'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'sampling_rate'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'padding_value'	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feat_extract_tester.prepare_inputs_for_common()
         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)

         self.assertTrue(all(len(UpperCamelCase__	) == len(UpperCamelCase__	) for x, y in zip(UpperCamelCase__   ,	processed_features[input_name]	)	)	)

         a_						     =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__	)
         a_						     =			BatchFeature({input_name: speech_inputs}   ,	tensor_type='np'	)

         a_						     =			processed_features[input_name]

         if len(batch_features_input.shape	) < 3:
                a_						     =			batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)







  @require_torch
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__	)
         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}   ,	tensor_type='pt'	)

         a_						     =			processed_features[input_name]

         if len(batch_features_input.shape	) < 3:
                a_						     =			batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)







  @require_tf
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__	)
         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}   ,	tensor_type='tf'	)

         a_						     =			processed_features[input_name]

         if len(batch_features_input.shape	) < 3:
                a_						     =			batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)







  def    _a       (			self   ,	UpperCamelCase__=False	):






         """simple docstring"""


         def _inputs_have_equal_length(UpperCamelCase__	):
                a_						     =			len(input[0]	)
                for input_slice in input[1:]:
                       if len(UpperCamelCase__	) != length:
                              return False
                return True

         def _inputs_are_equal(UpperCamelCase__   ,	UpperCamelCase__	):
                if len(UpperCamelCase__	) != len(UpperCamelCase__	):
                       return False

                for input_slice_a, input_slice_a in zip(UpperCamelCase__   ,	UpperCamelCase__	):
                       if not np.allclose(np.asarray(UpperCamelCase__	)   ,	np.asarray(UpperCamelCase__	)   ,	atol=1e-3	):
                              return False
                return True

         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__	)
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)

         a_						     =			self.feat_extract_tester.seq_length_diff
         a_						     =			self.feat_extract_tester.max_seq_length + pad_diff
         a_						     =			self.feat_extract_tester.min_seq_length
         a_						     =			self.feat_extract_tester.batch_size
         a_						     =			self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding=UpperCamelCase__	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[-1]	)	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	return_tensors='np'	)
         a_						     =			input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(UpperCamelCase__	):
                feat_extract.pad(UpperCamelCase__   ,	padding='max_length'	)[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=UpperCamelCase__   ,	return_tensors='np'	)
         a_						     =			input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(_inputs_are_equal(UpperCamelCase__   ,	UpperCamelCase__	)	)
         self.assertTrue(len(input_a[0]	) == pad_min_length	)
         self.assertTrue(len(input_a[1]	) == pad_min_length + pad_diff	)
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]	))	)
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)	)

         if feature_size > 1:
                self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size	)

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         a_						     =			feat_extract.pad(UpperCamelCase__   ,	pad_to_multiple_of=10	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	pad_to_multiple_of=10	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	pad_to_multiple_of=10   ,	max_length=UpperCamelCase__	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	pad_to_multiple_of=10   ,	max_length=UpperCamelCase__   ,	return_tensors='np'   ,	)
         a_						     =			input_a[input_name]

         self.assertTrue(all(len(UpperCamelCase__	) % 10 == 0 for x in input_a	)	)
         self.assertTrue(_inputs_are_equal(UpperCamelCase__   ,	UpperCamelCase__	)	)

         a_						     =			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(UpperCamelCase__	) == expected_mult_pad_length for x in input_a	)	)
         self.assertEqual(input_a.shape[:2]   ,	(batch_size, expected_mult_pad_length)	)

         if feature_size > 1:
                self.assertTrue(input_a.shape[2] == feature_size	)

         # Check padding value is correct
         a_						     =			(np.ones(self.feat_extract_tester.feature_size	) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]	)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)	)
             < 1e-3	)
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]	)[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)	)
             < 1e-3	)
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]	)[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)	)
             < 1e-3	)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)	) < 1e-3	)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)	)
             < 1e-3	)







  def    _a       (			self   ,	UpperCamelCase__=False	):






         """simple docstring"""


         def _inputs_have_equal_length(UpperCamelCase__	):
                a_						     =			len(input[0]	)
                for input_slice in input[1:]:
                       if len(UpperCamelCase__	) != length:
                              return False
                return True

         def _inputs_are_equal(UpperCamelCase__   ,	UpperCamelCase__	):
                if len(UpperCamelCase__	) != len(UpperCamelCase__	):
                       return False

                for input_slice_a, input_slice_a in zip(UpperCamelCase__   ,	UpperCamelCase__	):
                       if not np.allclose(np.asarray(UpperCamelCase__	)   ,	np.asarray(UpperCamelCase__	)   ,	atol=1e-3	):
                              return False
                return True

         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__	)
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)

         # truncate to smallest
         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[0]	)   ,	truncation=UpperCamelCase__	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[0]	)	)
         a_						     =			input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase__	)	)

         # truncate to smallest with np
         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[0]	)   ,	return_tensors='np'   ,	truncation=UpperCamelCase__   ,	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[0]	)   ,	return_tensors='np'	)
         a_						     =			input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]	)	)

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase__	)	)

         # truncate to middle
         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[1]	)   ,	truncation=UpperCamelCase__   ,	return_tensors='np'   ,	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[1]	)   ,	truncation=UpperCamelCase__	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[1]	)   ,	return_tensors='np'	)
         a_						     =			input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]	)	)
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(_inputs_are_equal(UpperCamelCase__   ,	UpperCamelCase__	)	)

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertTrue(len(input_a[-1]	) == len(speech_inputs[-1]	)	)

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(UpperCamelCase__	):
                feat_extract.pad(UpperCamelCase__   ,	truncation=UpperCamelCase__	)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(UpperCamelCase__	):
                feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	truncation=UpperCamelCase__	)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(UpperCamelCase__	):
                feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	truncation=UpperCamelCase__	)[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(UpperCamelCase__	):
                feat_extract.pad(UpperCamelCase__   ,	padding='max_length'   ,	truncation=UpperCamelCase__	)[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         a_						     =			12
         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[0]	)   ,	pad_to_multiple_of=UpperCamelCase__   ,	truncation=UpperCamelCase__   ,	)
         a_						     =			input_a[input_name]

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=len(speech_inputs[0]	)   ,	pad_to_multiple_of=UpperCamelCase__   ,	)
         a_						     =			input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         a_						     =			len(speech_inputs[0]	)
         if expected_length % pad_to_multiple_of != 0:
                a_						     =			((len(speech_inputs[0]	) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]	) == expected_length	)
         self.assertTrue(_inputs_have_equal_length(UpperCamelCase__	)	)
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase__	)	)







  def    _a       (			self	):






         """simple docstring"""


         self._check_padding(numpify=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         self._check_padding(numpify=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         self._check_truncation(numpify=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         self._check_truncation(numpify=UpperCamelCase__	)







  @require_torch
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			self.feat_extract_tester.prepare_inputs_for_common()
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	return_tensors='np'	)[input_name]
         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	return_tensors='pt'	)[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1e-2	)







  @require_tf
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feature_extraction_class(**self.feat_extract_dict	)
         a_						     =			self.feat_extract_tester.prepare_inputs_for_common()
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	return_tensors='np'	)[input_name]
         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	return_tensors='tf'	)[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_tf.numpy().astype(np.floataa	).sum()	) < 1e-2	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feat_extract_dict
         a_						     =			True
         a_						     =			self.feature_extraction_class(**UpperCamelCase__	)
         a_						     =			self.feat_extract_tester.prepare_inputs_for_common()
         a_						     =			[len(UpperCamelCase__	) for x in speech_inputs]
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)

         a_						     =			feat_extract.pad(UpperCamelCase__   ,	padding='longest'   ,	return_tensors='np'	)
         self.assertIn('attention_mask'   ,	UpperCamelCase__	)
         self.assertListEqual(list(processed.attention_mask.shape	)   ,	list(processed[input_name].shape[:2]	)	)
         self.assertListEqual(processed.attention_mask.sum(-1	).tolist()   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.feat_extract_dict
         a_						     =			True
         a_						     =			self.feature_extraction_class(**UpperCamelCase__	)
         a_						     =			self.feat_extract_tester.prepare_inputs_for_common()
         a_						     =			[len(UpperCamelCase__	) for x in speech_inputs]
         a_						     =			feat_extract.model_input_names[0]

         a_						     =			BatchFeature({input_name: speech_inputs}	)
         a_						     =			min(UpperCamelCase__	)

         a_						     =			feat_extract.pad(
             UpperCamelCase__   ,	padding='max_length'   ,	max_length=UpperCamelCase__   ,	truncation=UpperCamelCase__   ,	return_tensors='np'	)
         self.assertIn('attention_mask'   ,	UpperCamelCase__	)
         self.assertListEqual(
             list(processed_pad.attention_mask.shape	)   ,	[processed_pad[input_name].shape[0], max_length]	)
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()   ,	[max_length for x in speech_inputs]	)












'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




